Course coordinator
Dr Anne Kruger
Course overview
- Study period
- Semester 1, 2025 (24/02/2025 - 21/06/2025)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Communication & Arts School
This course applies journalistic research and storytelling techniques to the vast array of available digital information to empower students to generate advanced data-driven reporting. Students will find, collect, clean, analyse, filter and visualise data sets to produce compelling public-interest journalism. The course also considers the practice of data-driven journalism in the context of its history, the current media landscape, emerging technologies and the ethical codes and concerns that impact on this evolving journalistic specialisation.
In a crowded media landscape, data journalism is an increasingly important reporting practice. Good data journalism can contextualise issues, make sense of complex problems, offer solutions, hold power to account, fight disinformation and shape our understanding of the human experience. For many journalists, proficiency in data journalism is also a huge professional advantage. In this project-based course, you’ll learn the foundations of data-driven journalism (how to find, collect, scrape, clean, filter and visualise data) and the latest Open Source Investigative Technique (OSINT) tools,while conceptualising, producing and visualising your own data-driven investigation. In addition to developing a portfolio piece of journalistic work, you will also consider the place of data-driven journalism in the contemporary media landscape, the genre's history, the implications of new technology on future data-driven reporting and the ethical issues that may be involved in finding, accessing and publishing data sets.ﾠ
Course requirements
Assumed background
As this is a specialist journalism subject, prior experience with journalistic practice (an understanding of news values and formats, finding and interviewing sources, writing news articles and/or feature stories, and journalistic codes of practice and ethics) is assumed. Experience with maths, data, coding or statistics is welcome but certainly not required (in fact, even some of the world's best data journalists admit to harbouring a fear of numbers); however, an open mind with respect to these fields will be an advantage.ﾠ
Prerequisites
You'll need to complete the following courses before enrolling in this one:
COMU1130.
Prerequisites are waived for students who commenced prior to 2017.
Recommended prerequisites
We recommend completing the following courses before enrolling in this one:
JOUR1710
Incompatible
You can't enrol in this course if you've already completed the following:
JOUR3290
Restrictions
Restricted to students in the Bachelor of Journalism
Course contact
Course staff
Lecturer
Dr Anne Kruger
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Attendance at lectures and tutorials in this course is strongly advised; regular attendance is the easiest way to keep pace with the course content and get regular feedback on your own data-driven investigation. Please note that while lecture material will be recorded, tutorials will not—however, supplementary materials will be made available via Blackboard.ﾠ
Whilst every effort is made to place students in their preferred activity, it is not always possible for a student to be enrolled in their tutorial of choice. If you require assistance, please ensure that you email timetabling.commarts@enquire.uq.edu.au from your UQ student email with:
- Your name
- Your student ID
- The course code
- A list of three tutorial preferences (in order of preference)
- Reason for the change – e.g. timetable clash, elite athlete status, SAP
Teaching staff do not have access to change tutorials or help with timetables; all timetabling changes must be processed through the Timetabling Team.
Aims and outcomes
The course aims to develop student's ability to critique existing, and construct their own original data journalism work.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Critique existing examples of data journalism (in their professional, social and ethical contexts), and understand the role that data plays in story construction
LO2.
Locate and obtain relevant data for informing journalistic investigation
LO3.
Apply relevant sense-making processes to data to reveal data narratives that are of public significance and relevance
LO4.
Construct original and engaging data-led stories, supported by visualisations
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Project | Data Journalism Pitch | 20% |
7/04/2025 4:00 pm |
| Portfolio | Story (45%) and Data Visualisation (15%) | 60% 1000-1500 words |
30/05/2025 4:00 pm |
| Notebook/ Logbook | Research Journal and Sources List | 20% |
30/05/2025 4:00 pm |
Assessment details
Data Journalism Pitch
- Mode
- Written
- Category
- Project
- Weight
- 20%
- Due date
7/04/2025 4:00 pm
Task description
Write a succinct pitch for a data journalism project. While the topic and tone of your story are up to you, your work should explore a social issue with some public interest value.
Your pitch must include several features:
- A brief overview of the story idea.
- A potential publication venue for your story (while there is no expectation that you must publish your work, you will be assessed against the style and tone of your chosen publication).
- An example of a data set that could be used in the construction of the story (a link to the data set must be included in the pitch). Your data set should NOT be from a survey you have conducted yourself.
- Three research questions that will help shape the direction of your story.
- The names and contact details of five authoritative sources who can be interviewed for the story.
- Two examples of published visualisations that might inspire your own visualisation (with linkes and/or screenshots).
- A brief timeline outlining your approach to compiling the story.
A template will be provided on Blackboard. Please remember that, while you should attempt to be thorough, imaginative and forward-thinking about your research project before submitting the pitch, the nature of such projects is that they will evolve throughout the research and writing process. You may make alterations to the project direction as you begin to compile your research and based on the feedback you will receive from your tutor.
Students will be assessed using the rubric, available in on Blackboard, which includes the weightings of each of the assessment categories. Feedback on draft submissions will be available in designated tutorial workshops.
This assessment task evaluates students' abilities, skills and knowledge without the aid of Artificial Intelligence (AI). Within the pitch, students are advised that the use of AI or Machine Translation (MT) technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct. However, students may conceptualise an investigation that uses AI as a primary or secondary source, and may include their intent to do so within this assessment without penalty. For more information about how AI can be used in Assessment 2 and 3, please refer to those assessment items.
Submission guidelines
Submit via TurnItIn on Blackboard.
TurnItIn Receipts:
Assignments for this course will be submitted electronically via Blackboard and using TurnItIn. Before submitting any assignments for this course you must ensure you have completed UQ's compulsory online Academic Integrity Tutorial.
When you successfully submit your assessment to TurnItIn you will see text confirming your submission is complete, before being redirected to your Assignment inbox. On this page you can:
- View the name of the submitted file
- View date and time of the upload
- Resubmit your paper (if necessary)
- Download your submitted paper
- Download digital receipt.
If you cannot see your submission in your Assignment inbox you should regard your submission as unsuccessful. Students are responsible for retaining evidence of submission by the due date for all assessment items, in the required form (e.g. screenshot, email, photo, and an unaltered copy of submitted work).
If the submission was not successful:
- Note the error message (preferably take a screenshot).
- Go to your assignment page and see if it is possible to submit again.
- If you cannot submit again email your course coordinator immediately with the assignment attached.
Please visit this webpage for further advice on how to submit your TurnItIn assignment.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Story (45%) and Data Visualisation (15%)
- Mode
- Product/ Artefact/ Multimedia
- Category
- Portfolio
- Weight
- 60% 1000-1500 words
- Due date
30/05/2025 4:00 pm
Task description
You will be required to construct an original data journalism story using existing data. The story will draw on skills you have acquired during the semester in sourcing, cleaning, sense-making and production of data journalism.
The story must be between 1000-1500 words. You must have at least 5 primary sources (e.g. data sets, original interviews) in your story.
You must also produce a data visualisation to complement your story, using the free software options presented in class, or another platform of your choosing.
Drafts will be read but drafts must be submitted before or during the final tutorial. No drafts submitted after this will be reviewed.
Students will be assessed using the rubric, available in the course profile and in Blackboard, which includes the weightings of each of the assessment categories. Feedback on draft submissions will be available in designated tutorial workshops.
This task has been designed to be challenging, authentic and complex. Whilst students may use AI and Machine Translation (MT) technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance. As discussed in lectures, whilst Generative AI can be a powerful news gathering tool and primary source, it also has the capacity to undermine audience trust in news and information, and the use of Generative AI and MT must be considered and transparent. Should you wish you use AI or MT in your assessment, you must: consult with your tutor to make an AI integration plan; reference the use of an AI and MT-generated text, audio, video or imagery in your sources page; and clearly communicate the nature, use and rationale for using AI-generated material within your story.
Submission guidelines
Submit via TurnItIn on Blackboard.
TurnItIn Receipts:
Assignments for this course will be submitted electronically via Blackboard and using TurnItIn. Before submitting any assignments for this course you must ensure you have completed UQ's compulsory online Academic Integrity Tutorial.
When you successfully submit your assessment to TurnItIn you will see text confirming your submission is complete, before being redirected to your Assignment inbox. On this page you can:
- View the name of the submitted file
- View date and time of the upload
- Resubmit your paper (if necessary)
- Download your submitted paper
- Download digital receipt.
If you cannot see your submission in your Assignment inbox you should regard your submission as unsuccessful. Students are responsible for retaining evidence of submission by the due date for all assessment items, in the required form (e.g. screenshot, email, photo, and an unaltered copy of submitted work).
If the submission was not successful:
- Note the error message (preferably take a screenshot).
- Go to your assignment page and see if it is possible to submit again.
- If you cannot submit again email your course coordinator immediately with the assignment attached.
Please visit this webpage for further advice on how to submit your TurnItIn assignment.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Research Journal and Sources List
- Mode
- Written
- Category
- Notebook/ Logbook
- Weight
- 20%
- Due date
30/05/2025 4:00 pm
Task description
With a large data-driven project, it is sometimes possible to invest time, resources and innovation into aspects of a story that don't necessarily deliver results that are obvious from the final product. In order to ensure you received credit for any of the leads or angles you pursued that are not obvious from the story and graphic you submitted, you will use the templates provided on Blackboard to document:
- Your ongoing progress on your data-driven project
- Evidence of analysis of your data set
- A list of primary (e.g. interview) and secondary (e.g. documents) sources you have used for your story
Students will be assessed using the rubric, available in the course profile and in Blackboard, which includes the weightings of each of the assessment categories. Feedback on draft submissions will be available in designated tutorial workshops.
This task has been designed to be challenging, authentic and complex. Whilst students may use AI and Machine Translation (MT) technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance. As discussed in lectures, whilst Generative AI can be a powerful news gathering tool and primary source, it also has the capacity to undermine audience trust in news and information, and the use of Generative AI and MT must be considered and transparent. Should you wish you use AI and MT in your assessment, you must: consult with your tutor to make an AI and MT integration plan; reference the use of an AI and MT-generated text, audio, video or imagery in your sources page; and clearly communicate the nature, use and rationale for using AI and MT-generated material within your story.
Submission guidelines
Submit via TurnItIn on Blackboard.
TurnItIn Receipts:
Assignments for this course will be submitted electronically via Blackboard and using TurnItIn. Before submitting any assignments for this course you must ensure you have completed UQ's compulsory online Academic Integrity Tutorial.
When you successfully submit your assessment to TurnItIn you will see text confirming your submission is complete, before being redirected to your Assignment inbox. On this page you can:
- View the name of the submitted file
- View date and time of the upload
- Resubmit your paper (if necessary)
- Download your submitted paper
- Download digital receipt.
If you cannot see your submission in your Assignment inbox you should regard your submission as unsuccessful. Students are responsible for retaining evidence of submission by the due date for all assessment items, in the required form (e.g. screenshot, email, photo, and an unaltered copy of submitted work).
If the submission was not successful:
- Note the error message (preferably take a screenshot).
- Go to your assignment page and see if it is possible to submit again.
- If you cannot submit again email your course coordinator immediately with the assignment attached.
Please visit this webpage for further advice on how to submit your TurnItIn assignment.
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 28 days. Extensions are given in multiples of 24 hours.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 24 |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 25 - 44 |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 45 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
- Where fractional marks occur in the calculation of the final grade, a mark of x.5% or greater will be rounded up to (x+1)%. A percentage mark of less than x.5% will be rounded down to x%.
- Where no assessable work is received, a Grade of X will apply.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
- Further information regarding the assessment, including marking criteria and/or marking rubrics are available in the ‘Assessment’ folder in Blackboard for this course.
- Marks Cannot Be Changed After Being Released: Marks are not open to negotiation with course staff. If you wish to discuss the feedback you have received, you should make an appointment to speak with the Course Coordinator.
- Assessment Re-mark: If you are considering an Assessment Re-mark, please follow the link to important information you should consider before submitting a request.
- Integrity Pledge: Assignments for this course will be submitted electronically via Blackboard and using Turnitin. Before submitting any assignments for this course, you must ensure you have completed UQ's compulsory online Academic Integrity Modules.ﾠIn uploading an assignment via Turnitin you are certifying that it is your original work, that it has not been copied in whole or part from another person or source except where this is properly acknowledged, and that it has not in whole or part been previously submitted for assessment in any other course at this or any other university.
- Withholding marks prior to finalisation of grades: Per UQ Assessment Procedures – Release of Assessment Item Marks and Grades: The final assessment item and the marks for the assessment item are to be released only after the final grade for the course has been released.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Library resources are available on the UQ Library website.
Additional learning resources information
Readings will be provided on Blackboard and will be discussed in tutorials.
ﾠ
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 |
Lecture |
Week 1 Lecture: Introduction What is data journalism? Why is it important? And how can you use it? Readings/Ref: Handbook1; Journalism; Handbook2 |
Tutorial |
Week 1 Tutorial: Data: An Introduction Data isn't just numbers. An introduction to the key kinds of data, the course format and your peers/tutor. |
|
Multiple weeks From Week 2 To Week 3 |
Lecture |
Week 2 Lecture: Data Journalism Data journalism processes, practice and possibilities. What makes good data journalism? Types of data journalism stories and presentation. Readings/Ref: Handbook1; Journalism; Handbook2 |
Tutorial |
Week 2-3 Tutorial: Beyond the Numbers Introduction to data journalism. Analysis of data journalism examples. |
|
Week 4 (17 Mar - 23 Mar) |
Lecture |
Week 4 Lecture: Sourcing Data Why should journalists use data? Types of data and file types, open-source data, using government data and finding data stories. Readings/Ref: Handbook1; Journalism; Handbook2 |
Tutorial |
Week 4 Tutorial: The Great Data Hunt Finding data sets that can deliver unexpected stories. |
|
Week 5 (24 Mar - 30 Mar) |
Lecture |
Week 5 Lecture: Wrangling Data Collecting your own data, cleaning data and making data useable |
Tutorial |
Week 5 Tutorial: Data Clean-up Scraping your own data set, how to clean your data and starting to listen to what it might tell you. |
|
Week 6 (31 Mar - 06 Apr) |
Lecture |
Week 6 Lecture: Asking the Right Questions How to interrogate your data. What questions to ask and how to look for answers (trends, comparisons, outliers) Readings/Ref: Handbook1; Journalism; Handbook2 |
Tutorial |
Week 6 Tutorial: Revealing Your Data's Stories and Secrets Finding trends, digging up stories from data. Project planning: opportunities to workshop your investigation. |
|
Week 7 (07 Apr - 13 Apr) |
Lecture |
Week 7 Lecture: Bad Data The ethics of using data. Considering privacy, accessibility, data gaps and other ethical implications for using data. Readings/Ref: Handbook1; Journalism; Handbook2 |
Tutorial |
Week 7 Tutorial: The Curly Questions Grappling with ethical issues in data-driven reporting |
|
Week 8 (14 Apr - 20 Apr) |
Lecture |
Week 8 Lecture: Analysis Drawing out meaning from your data and the benefits (and perils) of using statistics and polling. Readings/Ref: Handbook1; Journalism; Handbook2 |
Tutorial |
Week 8: NO TUTORIALS No tutorials due to public holiday. |
|
Mid-sem break (21 Apr - 27 Apr) |
No student involvement (Breaks, information) |
MID-SEMESTER BREAK |
Week 9 (28 Apr - 04 May) |
Lecture |
Week 9 Lecture: Visualising Data Using visualisations and interactive applications to bring your data to life. Readings/Ref: Handbook1; Journalism; Handbook2 |
Tutorial |
Week 9 Tutorial: Hands-On Data Exploration Exploring your own data and contextualising it with additional research Readings/Ref: Handbook1; Journalism; Handbook2 |
|
Week 10 (05 May - 11 May) |
No student involvement (Breaks, information) |
Week 10: NO LECTURE OR TUTORIALS No lecture or tutorials due to the public holiday. |
Week 11 (12 May - 18 May) |
Lecture |
Week 11 Lecture: Storytelling with Data Writing a data-driven story and contextualising data Readings/Ref: Handbook1; Journalism; Handbook2 |
Tutorial |
Week 11 Tutorial: Visualisations and Storytelling Planning and beginning to execute your visualisation |
|
Week 12 (19 May - 25 May) |
Lecture |
Week 12 Lecture: Data Journalism in the Newsroom Data journalism in practice. Readings/Ref: Handbook1; Journalism; Handbook2 |
Tutorial |
Week 12 Tutorial: Telling True Stories Techniques to connect readers to your story. Troubleshooting your story. |
|
Week 13 (26 May - 01 Jun) |
Lecture |
Week 13 Lecture: The Future of Data Journalism Emerging fields in data-driven reporting. Readings/Ref: Handbook1; Journalism; Handbook2 |
Tutorial |
Week 13 Tutorial: Assessment Bootcamp Troubleshooting your data-driven story and visualisation and workshopping your final stories. |
|
Revision week (02 Jun - 08 Jun) |
No student involvement (Breaks, information) |
Revision week: NO LECTURE OR TUTORIALS |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.
Course guidelines
Communication Expectations
While you are a student at UQ, all communication must be conducted according to the UQ Student Code of Conduct. The UQ Library has a helpful Communicate and collaborate online module.
- Email is the primary way for you to send messages to, and receive information from, the School and our staff.
- You must use your UQ email address (not a private address) to communicate with staff.
- You should add a clear subject line, including course code, and a 2-3 word statement.
- You can send email at any time, however please do not expect responses outside normal working hours (Monday to Friday from ~8am to ~5pm).
- Emails that constitute bullying, harassment or discrimination against staff contravene the Student Code of Conduct. Emails like this will be reported to the University, and the matter will be pursued as misconduct.